Network Security Situation Awareness Forecast Algorithm using Neural Network

In order to predict network threats more accurately, this study proposes a prediction model using artificial bee colony(ABC) algorithm to optimize RBFNNK. Step one, we use the self-encoder to reduce the dimension of the input data, so as to improve the data processing efficiency. Step two, we use ABC to optimize the parameters of RBFNNK. After optimization, implementation efficiency and accuracy of the RBFNNK is improved. Finally, because the input data values of the RBFNNK is nonlinear time series values, RBFNNK is used to find out the nonlinear mapping relationship of network security situation values, in this way we can realize the network security situation awareness and prediction process. Through experiments, the designed algorithm is compared with the existing prediction methods. From the experimental results, we can see that the RBFNNK designed in this study is effective and has very high accuracy.